Unlocking the research potential of unstructured patient data to improve health and treatment outcomes

医院的电子健康记录（EHRs）包含大量常规收集的丰富信息，这些信息有可能推动患者护理和研究的改进。二级保健卫生数据空间尤其异构，因为它包括结构良好的变量（例如来自诊断和实验室测试、手动编码的数据）和半/非结构化数据（例如图像或临床信函）。虽然前者通常是医疗保健数据科学的核心，但后一组非结构化数据意味着我们在二级医疗方面的知识存在重要差距。门诊信和住院临床记录是半结构化的自由文本文件，其中包含有关患者、治疗和结果的关键临床信息。虽然以电子方式记录用于临床目的，但其非结构化性质和敏感内容意味着该数据源通常无法用于支持研究或服务改进的二次使用。例如，确定在当前大流行期间需要隔离的某些患者依赖于医院临床团队手动审查数千封患者来信，这需要大量资源和时间。对于医院门诊病人的病情，令人惊讶的是，没有一个全国性的系统来记录诊断或处方药物：必要的信息只能以字母形式提供免费文本。在临床相关问题的引导下，自动文本挖掘技术可以解锁隐藏在海量数据中的相关信息，进而辅助临床决策。在这个项目中，通过一个将计算机科学、二级保健（NHS）和流行病学联系起来的跨学科团队，候选人将帮助开发和验证一个知识管理框架，以安全地解锁存储在电子病历中的信息，测试和实施文本挖掘方法，以获取用于研究的编码数据，将其与其他健康数据集成，并通过案例研究展示其影响和益处。我们将把重点放在索尔福德皇家基金会信托的医院数据上，这是一个全球数字范例（GDE）网站，我们将开发和验证一个共享系统，利用文本挖掘从医院门诊信件和住院记录中提取诊断、药物和其他相关信息。虽然我们将首先关注肌肉骨骼（MSK）疾病，但该项目也将探索必要的迁移学习，以使系统适合其他专业和服务。

Electronic health records (EHRs) in hospitals contain a wealth of rich, routinely collected information that have the potential to drive improvements in patient care and research. The secondary care health data space is especially heterogeneous, as it includes well-structured variables (e.g. from diagnostic and laboratory tests, manually coded data) and semi/un-structured data (e.g. images or clinical letters). While the former is often the core of healthcare data science, the latter group of unstructured data means there are important gaps in our knowledge about aspects of secondary care. Outpatient letters and in-patient clinical notes are semi-structured free-text documents that contain key clinical information about patients, their treatments and outcomes. Whilst recorded electronically for clinical purposes, their unstructured nature and sensitive content mean that this data source is often inaccessible for secondary use to support research or service improvement. For example, identifying certain patients who needed to shield during the current pandemic relied on hospital clinical teams reviewing thousands of patient letters manually, with significant resource and time implications. For conditions managed in hospital outpatients, there is surprisingly no national system for recording diagnoses or prescribed medications: the necessary information is only available as free text in letters. Guided by relevant clinical questions, automated text-mining techniques can unlock pertinent information hidden in the massive amount of data, which in turn can assist clinical decision making. In this project, through an interdisciplinary team linking computer science, secondary care (NHS) and epidemiology, the candidate will help develop and validate a knowledge management framework to safely unlock information stored within EHRs, test and implement text-mining methods to obtain coded data for research, integrate it with other health data and demonstrate its impact and benefits through case studies. We will focus on hospital data from Salford Royal Foundation Trust, a Global Digital Exemplar (GDE) site, to develop and validate a sharable system for extracting diagnoses, medications and other pertinent information from hospital outpatient letters and inpatient records using text mining. While we will initially focus on musculoskeletal (MSK) conditions, the project will also explore necessary transfer-learning to tailor the system for other specialities and services.